ArticleBioMed research international2014
In silico investigation of traditional Chinese medicine compounds to inhibit human histone deacetylase 2 for patients with Alzheimer's disease.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Comparative differential cytotoxicity of clinically used SERMs in human cancer lines of different origin and its predictive molecular docking studies of key target genes involved in cancer progression and treatment responses.Current research in pharmacology and drug discovery · 2022Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Biocomputational Screening of Natural Compounds against Acetylcholinesterase.Molecules (Basel, Switzerland) · 2021Article
- Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine.Molecules (Basel, Switzerland) · 2018Review
- Alzheimer: A Decade of Drug Design. Why Molecular Topology can be an Extra Edge?Current neuropharmacology · 2018Review
- Computational Investigation into the Interactions of Traditional Chinese Medicine Molecules of WenQingYin with GluR2.International journal of molecular sciences · 2017Article
- Epigenetic Mechanisms of Integrative Medicine.Evidence-based complementary and alternative medicine : eCAM · 2017Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human histone deacetylase 2 (HDAC2) has been identified as being associated with Alzheimer's disease (AD), a neuropathic degenerative disease. In this study, we screen the world's largest Traditional Chinese Medicine (TCM) database for natural compounds that may be useful as lead compounds in the search for inhibitors of HDAC2 function. The technique of molecular docking was employed to select the ten top TCM candidates. We used three prediction models, multiple linear regression (MLR), support vector machine (SVM), and the Bayes network toolbox (BNT), to predict the bioactivity of the TCM candidates. Molecular dynamics simulation provides the protein-ligand interactions of compounds. The bioactivity predictions of pIC50 values suggest that the TCM candidatesm, (-)-Bontl ferulate, monomethylcurcumin, and ningposides C, have a greater effect on HDAC2 inhibition. The structure variation caused by the hydrogen bonds and hydrophobic interactions between protein-ligand interactions indicates that these compounds have an inhibitory effect on the protein.
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